Add README.md

Author: Gioee

README.md

@@ -0,0 +1,130 @@
+# SQLite Agent
+
+**SQLite Agent** is a cross-platform SQLite extension that brings autonomous AI agents to your database. It enables databases to run multi-step, tool-using agents that fetch data from external sources (web, APIs, etc.) through [sqlite-mcp](https://github.com/sqliteai/sqlite-mcp) and populate database tables. The extracted data can then be analyzed by LLMs, displayed in UIs, or used in applications.
+
+## Highlights
+
+* **Autonomous Agents** – Run AI agents that can plan and execute multi-step tasks
+* **MCP Integration** – Works with [sqlite-mcp](https://github.com/sqliteai/sqlite-mcp) for external tool access
+* **AI Powered** – Uses [sqlite-ai](https://github.com/sqliteai/sqlite-ai) for LLM inference and embeddings
+* **Table Extraction Mode** – Automatically fetch and populate database tables
+* **Auto-Embeddings** – Optionally uses [sqlite-vector](https://github.com/sqliteai/sqlite-vector) to generate vector embeddings automatically when inserting data for BLOB columns named `*_embedding`
+* **Auto-Vector Indexing** – Initialize vector search indices automatically
+
+## 🚀 Quick Start
+
+### Installation
+
+#### Pre-built Binaries
+
+Download the appropriate pre-built binary for your platform from the [Releases](https://github.com/sqliteai/sqlite-agent/releases) page: we do support **Linux**, **macOS**, **Windows**, **Android** and **iOS**.
+
+### Basic Usage
+
+```sql
+-- Load required extensions
+SELECT load_extension('./dist/mcp');
+SELECT load_extension('./dist/ai');
+SELECT load_extension('./dist/agent');
+
+-- Initialize AI model
+SELECT llm_model_load('/path/to/model.gguf', 'gpu_layers=99');
+
+-- Connect to MCP server
+SELECT mcp_connect('http://localhost:8000/mcp');
+
+-- Run an autonomous agent (text-only mode)
+SELECT agent_run('Find affordable apartments in Rome with AC', 8);
+-- Agent uses MCP tools to accomplish the task and returns result
+```
+
+## 📖 API Reference
+
+### Available Functions
+
+| Function | Description |
+|----------|-------------|
+| `agent_version()` | Returns extension version |
+| `agent_run(goal, [table_name], [max_iterations], [system_prompt])` | Run autonomous AI agent |
+
+See [API.md](API.md) for complete API documentation with examples.
+
+## Complete Example: AirBnb MCP Server
+
+This example demonstrates combining agent, MCP, AI, and vector extensions:
+
+```sql
+-- Load all extensions
+.load ../sqlite-mcp/dist/mcp
+.load ./dist/agent
+.load ../sqlite-ai/dist/ai
+.load ../sqlite-vector/dist/vector
+
+-- Initialize AI model (one-time)
+SELECT llm_model_load('/path/to/model.gguf', 'gpu_layers=99');
+
+-- Connect to MCP server (one-time)
+SELECT mcp_connect('http://localhost:8000/mcp');
+
+-- Create table with embedding column
+CREATE TABLE listings (
+  id INTEGER PRIMARY KEY,
+  title TEXT,
+  description TEXT,
+  location TEXT,
+  property_type TEXT,
+  amenities TEXT,
+  price REAL,
+  rating REAL,
+  embedding BLOB
+);
+
+-- Run agent to fetch and populate data
+-- Embeddings and vector index are created automatically!
+SELECT agent_run(
+  'Find affordable apartments in Rome under 100 EUR per night',
+  'listings',
+  8
+);
+-- Returns: "Inserted 5 rows into listings"
+
+-- Semantic search (works immediately!)
+SELECT title, location, price, v.distance
+FROM vector_full_scan('listings', 'embedding',
+  llm_embed_generate('cozy modern apartment', ''), 5) v
+JOIN listings l ON l.rowid = v.rowid
+ORDER BY v.distance ASC;
+```
+
+Run the full demo:
+```bash
+make airbnb
+```
+
+## How It Works
+
+The agent operates through an iterative loop:
+
+1. Receives goal and available tools from MCP
+2. Decides which tool to call
+3. Executes tool via sqlite-mcp extension
+4. Receives tool result
+5. Continues until goal achieved or max iterations reached
+6. Returns final result
+
+## Related Projects
+
+- [sqlite-mcp](https://github.com/sqliteai/sqlite-mcp) – Model Context Protocol client for SQLite
+- [sqlite-ai](https://github.com/sqliteai/sqlite-ai) – AI/ML inference and embeddings for SQLite
+- [sqlite-vector](https://github.com/sqliteai/sqlite-vector) – Vector search capabilities for SQLite
+- [sqlite-sync](https://github.com/sqliteai/sqlite-sync) – Cloud synchronization for SQLite
+- [sqlite-js](https://github.com/sqliteai/sqlite-js) – JavaScript engine integration for SQLite
+
+## Support
+
+- [GitHub Issues](https://github.com/sqliteai/sqlite-agent/issues)
+- [SQLite Extension Load Guide](https://github.com/sqliteai/sqlite-extensions-guide)
+
+## License
+
+This project is licensed under the [Elastic License 2.0](./LICENSE). You can use, copy, modify, and distribute it under the terms of the license for non-production use. For production or managed service use, please [contact SQLite Cloud, Inc](mailto:[email protected]) for a commercial license.